A significant number of hotel bookings are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.
The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.
The cancellation of bookings impact a hotel on various fronts:
The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.
Data Dictionary
# this will help in making the Python code more structured automatically (help adhere to good coding practices)
#%load_ext nb_black
import warnings
warnings.filterwarnings("ignore")
from statsmodels.tools.sm_exceptions import ConvergenceWarning
warnings.simplefilter("ignore", ConvergenceWarning)
# Libraries to help with reading and manipulating data
import pandas as pd
import numpy as np
# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns
# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# setting the precision of floating numbers to 5 decimal points
pd.set_option("display.float_format", lambda x: "%.5f" % x)
# Library to split data
from sklearn.model_selection import train_test_split
# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
# To tune different models
from sklearn.model_selection import GridSearchCV
# To get diferent metric scores
from sklearn.metrics import (
f1_score,
accuracy_score,
recall_score,
precision_score,
confusion_matrix,
roc_auc_score,
confusion_matrix,
precision_recall_curve,
roc_curve,
make_scorer,
)
#Connecting google colab
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
# Loading data
df_main = pd.read_csv("/content/drive/MyDrive/INNHotelsGroup.csv")
#making a copy of main data
data = df_main.copy()
# view first five rows of data
data.head()
| Booking_ID | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | INN00001 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 224 | 2017 | 10 | 2 | Offline | 0 | 0 | 0 | 65.00000 | 0 | Not_Canceled |
| 1 | INN00002 | 2 | 0 | 2 | 3 | Not Selected | 0 | Room_Type 1 | 5 | 2018 | 11 | 6 | Online | 0 | 0 | 0 | 106.68000 | 1 | Not_Canceled |
| 2 | INN00003 | 1 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2018 | 2 | 28 | Online | 0 | 0 | 0 | 60.00000 | 0 | Canceled |
| 3 | INN00004 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 211 | 2018 | 5 | 20 | Online | 0 | 0 | 0 | 100.00000 | 0 | Canceled |
| 4 | INN00005 | 2 | 0 | 1 | 1 | Not Selected | 0 | Room_Type 1 | 48 | 2018 | 4 | 11 | Online | 0 | 0 | 0 | 94.50000 | 0 | Canceled |
# view last five rows of data
data.tail()
| Booking_ID | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36270 | INN36271 | 3 | 0 | 2 | 6 | Meal Plan 1 | 0 | Room_Type 4 | 85 | 2018 | 8 | 3 | Online | 0 | 0 | 0 | 167.80000 | 1 | Not_Canceled |
| 36271 | INN36272 | 2 | 0 | 1 | 3 | Meal Plan 1 | 0 | Room_Type 1 | 228 | 2018 | 10 | 17 | Online | 0 | 0 | 0 | 90.95000 | 2 | Canceled |
| 36272 | INN36273 | 2 | 0 | 2 | 6 | Meal Plan 1 | 0 | Room_Type 1 | 148 | 2018 | 7 | 1 | Online | 0 | 0 | 0 | 98.39000 | 2 | Not_Canceled |
| 36273 | INN36274 | 2 | 0 | 0 | 3 | Not Selected | 0 | Room_Type 1 | 63 | 2018 | 4 | 21 | Online | 0 | 0 | 0 | 94.50000 | 0 | Canceled |
| 36274 | INN36275 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 207 | 2018 | 12 | 30 | Offline | 0 | 0 | 0 | 161.67000 | 0 | Not_Canceled |
#Code to get same random result every time
np.random.seed(1)
data.sample(n=50)
| Booking_ID | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30392 | INN30393 | 1 | 0 | 1 | 0 | Not Selected | 0 | Room_Type 1 | 53 | 2018 | 9 | 11 | Online | 0 | 0 | 0 | 94.32000 | 0 | Not_Canceled |
| 6685 | INN06686 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 63 | 2018 | 4 | 22 | Online | 0 | 0 | 0 | 105.30000 | 1 | Canceled |
| 8369 | INN08370 | 2 | 0 | 2 | 3 | Meal Plan 1 | 0 | Room_Type 4 | 55 | 2018 | 9 | 11 | Online | 0 | 0 | 0 | 106.24000 | 0 | Not_Canceled |
| 2055 | INN02056 | 2 | 0 | 0 | 2 | Not Selected | 0 | Room_Type 1 | 53 | 2017 | 12 | 29 | Online | 0 | 0 | 0 | 81.00000 | 1 | Not_Canceled |
| 10969 | INN10970 | 1 | 0 | 2 | 4 | Meal Plan 1 | 0 | Room_Type 1 | 245 | 2018 | 7 | 6 | Offline | 0 | 0 | 0 | 110.00000 | 0 | Canceled |
| 24881 | INN24882 | 2 | 0 | 3 | 7 | Meal Plan 1 | 0 | Room_Type 2 | 231 | 2018 | 8 | 1 | Online | 0 | 0 | 0 | 81.82000 | 2 | Canceled |
| 28658 | INN28659 | 2 | 0 | 0 | 3 | Meal Plan 2 | 0 | Room_Type 1 | 71 | 2018 | 5 | 10 | Offline | 0 | 0 | 0 | 126.00000 | 1 | Not_Canceled |
| 20853 | INN20854 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 66 | 2017 | 10 | 9 | Offline | 0 | 0 | 0 | 75.00000 | 0 | Canceled |
| 8501 | INN08502 | 2 | 0 | 0 | 3 | Meal Plan 1 | 0 | Room_Type 2 | 40 | 2018 | 1 | 14 | Online | 0 | 0 | 0 | 77.55000 | 1 | Not_Canceled |
| 1942 | INN01943 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 63 | 2018 | 8 | 9 | Online | 0 | 0 | 0 | 144.90000 | 2 | Not_Canceled |
| 15648 | INN15649 | 2 | 0 | 2 | 4 | Meal Plan 1 | 0 | Room_Type 1 | 209 | 2018 | 7 | 2 | Online | 0 | 0 | 0 | 66.53000 | 1 | Not_Canceled |
| 6116 | INN06117 | 2 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 9 | 2018 | 7 | 6 | Online | 0 | 0 | 0 | 139.00000 | 1 | Not_Canceled |
| 7868 | INN07869 | 2 | 0 | 2 | 4 | Meal Plan 1 | 0 | Room_Type 4 | 123 | 2018 | 5 | 22 | Online | 0 | 0 | 0 | 114.75000 | 1 | Canceled |
| 24527 | INN24528 | 2 | 0 | 0 | 3 | Meal Plan 1 | 0 | Room_Type 1 | 118 | 2018 | 6 | 28 | Online | 0 | 0 | 0 | 96.30000 | 0 | Canceled |
| 24227 | INN24228 | 2 | 0 | 1 | 0 | Meal Plan 1 | 0 | Room_Type 4 | 72 | 2018 | 10 | 9 | Online | 0 | 0 | 0 | 132.30000 | 3 | Not_Canceled |
| 17216 | INN17217 | 2 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2018 | 11 | 23 | Online | 0 | 0 | 0 | 120.00000 | 0 | Not_Canceled |
| 31124 | INN31125 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 7 | 2017 | 8 | 28 | Corporate | 1 | 1 | 2 | 65.00000 | 0 | Not_Canceled |
| 9101 | INN09102 | 1 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 12 | 2018 | 10 | 2 | Online | 0 | 0 | 0 | 6.00000 | 0 | Not_Canceled |
| 9474 | INN09475 | 2 | 0 | 0 | 2 | Meal Plan 2 | 0 | Room_Type 1 | 63 | 2017 | 9 | 4 | Offline | 0 | 0 | 0 | 116.00000 | 0 | Not_Canceled |
| 12782 | INN12783 | 2 | 0 | 0 | 3 | Not Selected | 0 | Room_Type 1 | 18 | 2018 | 1 | 21 | Online | 0 | 0 | 0 | 77.00000 | 1 | Not_Canceled |
| 28297 | INN28298 | 1 | 0 | 2 | 5 | Meal Plan 1 | 0 | Room_Type 1 | 68 | 2018 | 8 | 29 | Online | 0 | 0 | 0 | 92.35000 | 1 | Not_Canceled |
| 22021 | INN22022 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 42 | 2018 | 11 | 4 | Offline | 0 | 0 | 0 | 72.00000 | 0 | Not_Canceled |
| 26586 | INN26587 | 3 | 0 | 2 | 2 | Meal Plan 1 | 0 | Room_Type 4 | 53 | 2018 | 3 | 20 | Online | 0 | 0 | 0 | 124.10000 | 1 | Canceled |
| 16756 | INN16757 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 305 | 2018 | 11 | 4 | Offline | 0 | 0 | 0 | 89.00000 | 0 | Canceled |
| 20927 | INN20928 | 2 | 0 | 2 | 6 | Meal Plan 1 | 0 | Room_Type 1 | 106 | 2018 | 7 | 8 | Offline | 0 | 0 | 0 | 72.25000 | 2 | Not_Canceled |
| 35753 | INN35754 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 128 | 2018 | 6 | 20 | Online | 0 | 0 | 0 | 120.00000 | 0 | Canceled |
| 9359 | INN09360 | 1 | 0 | 1 | 0 | Meal Plan 1 | 0 | Room_Type 1 | 7 | 2018 | 5 | 16 | Online | 0 | 0 | 0 | 97.00000 | 1 | Not_Canceled |
| 21929 | INN21930 | 1 | 0 | 0 | 4 | Meal Plan 1 | 0 | Room_Type 4 | 48 | 2018 | 8 | 24 | Online | 0 | 0 | 0 | 149.40000 | 1 | Not_Canceled |
| 17501 | INN17502 | 2 | 0 | 0 | 3 | Not Selected | 0 | Room_Type 1 | 27 | 2018 | 3 | 22 | Online | 0 | 0 | 0 | 129.00000 | 0 | Canceled |
| 3355 | INN03356 | 2 | 1 | 2 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 173 | 2018 | 8 | 13 | Online | 0 | 0 | 0 | 114.75000 | 1 | Canceled |
| 22183 | INN22184 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 9 | 2018 | 6 | 1 | Online | 0 | 0 | 0 | 97.02000 | 1 | Not_Canceled |
| 7818 | INN07819 | 2 | 0 | 0 | 1 | Not Selected | 0 | Room_Type 1 | 57 | 2018 | 12 | 1 | Online | 0 | 0 | 0 | 79.20000 | 2 | Not_Canceled |
| 26360 | INN26361 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 245 | 2018 | 6 | 17 | Offline | 0 | 0 | 0 | 75.00000 | 0 | Canceled |
| 15193 | INN15194 | 2 | 0 | 0 | 3 | Meal Plan 2 | 0 | Room_Type 1 | 36 | 2017 | 10 | 13 | Offline | 0 | 0 | 0 | 112.00000 | 0 | Not_Canceled |
| 19873 | INN19874 | 2 | 0 | 0 | 1 | Not Selected | 0 | Room_Type 1 | 38 | 2018 | 7 | 2 | Online | 0 | 0 | 0 | 107.10000 | 1 | Not_Canceled |
| 8015 | INN08016 | 1 | 1 | 0 | 1 | Meal Plan 2 | 0 | Room_Type 1 | 29 | 2018 | 12 | 17 | Online | 0 | 0 | 0 | 130.00000 | 0 | Not_Canceled |
| 36151 | INN36152 | 2 | 0 | 2 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 26 | 2018 | 2 | 7 | Online | 0 | 0 | 0 | 64.64000 | 0 | Not_Canceled |
| 21254 | INN21255 | 1 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 6 | 2018 | 5 | 23 | Online | 0 | 0 | 0 | 97.02000 | 1 | Not_Canceled |
| 16881 | INN16882 | 2 | 0 | 1 | 0 | Meal Plan 1 | 0 | Room_Type 1 | 258 | 2018 | 10 | 16 | Offline | 0 | 0 | 0 | 110.00000 | 0 | Canceled |
| 9465 | INN09466 | 2 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 4 | 118 | 2018 | 10 | 29 | Online | 0 | 0 | 0 | 104.40000 | 1 | Not_Canceled |
| 18785 | INN18786 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 188 | 2018 | 6 | 15 | Offline | 0 | 0 | 0 | 130.00000 | 0 | Canceled |
| 28097 | INN28098 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 39 | 2018 | 3 | 14 | Offline | 0 | 0 | 0 | 85.00000 | 0 | Not_Canceled |
| 29239 | INN29240 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 32 | 2017 | 11 | 20 | Offline | 0 | 0 | 0 | 73.00000 | 0 | Not_Canceled |
| 664 | INN00665 | 2 | 1 | 1 | 1 | Meal Plan 1 | 1 | Room_Type 1 | 27 | 2018 | 8 | 8 | Online | 0 | 0 | 0 | 195.50000 | 2 | Not_Canceled |
| 8285 | INN08286 | 2 | 0 | 2 | 1 | Not Selected | 0 | Room_Type 1 | 240 | 2018 | 12 | 10 | Online | 0 | 0 | 0 | 67.50000 | 2 | Canceled |
| 4598 | INN04599 | 2 | 0 | 1 | 0 | Not Selected | 0 | Room_Type 1 | 127 | 2018 | 7 | 25 | Online | 0 | 0 | 0 | 94.50000 | 1 | Not_Canceled |
| 21340 | INN21341 | 2 | 2 | 0 | 3 | Meal Plan 1 | 0 | Room_Type 6 | 24 | 2018 | 4 | 14 | Online | 0 | 0 | 0 | 207.00000 | 2 | Not_Canceled |
| 12098 | INN12099 | 2 | 0 | 1 | 3 | Meal Plan 1 | 0 | Room_Type 1 | 262 | 2018 | 12 | 26 | Online | 0 | 0 | 0 | 73.95000 | 0 | Canceled |
| 2904 | INN02905 | 2 | 0 | 0 | 5 | Meal Plan 1 | 0 | Room_Type 4 | 41 | 2018 | 10 | 4 | Online | 0 | 0 | 0 | 139.50000 | 2 | Not_Canceled |
| 21902 | INN21903 | 2 | 0 | 2 | 3 | Meal Plan 1 | 0 | Room_Type 4 | 101 | 2018 | 5 | 5 | Online | 0 | 0 | 0 | 132.60000 | 1 | Not_Canceled |
#Checking the size of the data set
data.shape
(36275, 19)
#Checking the data types of the data set
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 36275 entries, 0 to 36274 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Booking_ID 36275 non-null object 1 no_of_adults 36275 non-null int64 2 no_of_children 36275 non-null int64 3 no_of_weekend_nights 36275 non-null int64 4 no_of_week_nights 36275 non-null int64 5 type_of_meal_plan 36275 non-null object 6 required_car_parking_space 36275 non-null int64 7 room_type_reserved 36275 non-null object 8 lead_time 36275 non-null int64 9 arrival_year 36275 non-null int64 10 arrival_month 36275 non-null int64 11 arrival_date 36275 non-null int64 12 market_segment_type 36275 non-null object 13 repeated_guest 36275 non-null int64 14 no_of_previous_cancellations 36275 non-null int64 15 no_of_previous_bookings_not_canceled 36275 non-null int64 16 avg_price_per_room 36275 non-null float64 17 no_of_special_requests 36275 non-null int64 18 booking_status 36275 non-null object dtypes: float64(1), int64(13), object(5) memory usage: 5.3+ MB
#Checking for duplicate values
data[data.duplicated()].count()
Booking_ID 0 no_of_adults 0 no_of_children 0 no_of_weekend_nights 0 no_of_week_nights 0 type_of_meal_plan 0 required_car_parking_space 0 room_type_reserved 0 lead_time 0 arrival_year 0 arrival_month 0 arrival_date 0 market_segment_type 0 repeated_guest 0 no_of_previous_cancellations 0 no_of_previous_bookings_not_canceled 0 avg_price_per_room 0 no_of_special_requests 0 booking_status 0 dtype: int64
#Checking for NUll values
data.isnull().sum()
Booking_ID 0 no_of_adults 0 no_of_children 0 no_of_weekend_nights 0 no_of_week_nights 0 type_of_meal_plan 0 required_car_parking_space 0 room_type_reserved 0 lead_time 0 arrival_year 0 arrival_month 0 arrival_date 0 market_segment_type 0 repeated_guest 0 no_of_previous_cancellations 0 no_of_previous_bookings_not_canceled 0 avg_price_per_room 0 no_of_special_requests 0 booking_status 0 dtype: int64
#Dropping the 'Booking ID' columns from the data set.
data = data.drop('Booking_ID', axis=1)
#Viewing what are the values in object data types
cat_columns = ['type_of_meal_plan', 'room_type_reserved', 'market_segment_type', 'booking_status']
for i in cat_columns:
print(data[i].value_counts())
print("*" * 50)
Meal Plan 1 27835 Not Selected 5130 Meal Plan 2 3305 Meal Plan 3 5 Name: type_of_meal_plan, dtype: int64 ************************************************** Room_Type 1 28130 Room_Type 4 6057 Room_Type 6 966 Room_Type 2 692 Room_Type 5 265 Room_Type 7 158 Room_Type 3 7 Name: room_type_reserved, dtype: int64 ************************************************** Online 23214 Offline 10528 Corporate 2017 Complementary 391 Aviation 125 Name: market_segment_type, dtype: int64 ************************************************** Not_Canceled 24390 Canceled 11885 Name: booking_status, dtype: int64 **************************************************
data.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| no_of_adults | 36275.00000 | 1.84496 | 0.51871 | 0.00000 | 2.00000 | 2.00000 | 2.00000 | 4.00000 |
| no_of_children | 36275.00000 | 0.10528 | 0.40265 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 10.00000 |
| no_of_weekend_nights | 36275.00000 | 0.81072 | 0.87064 | 0.00000 | 0.00000 | 1.00000 | 2.00000 | 7.00000 |
| no_of_week_nights | 36275.00000 | 2.20430 | 1.41090 | 0.00000 | 1.00000 | 2.00000 | 3.00000 | 17.00000 |
| required_car_parking_space | 36275.00000 | 0.03099 | 0.17328 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 |
| lead_time | 36275.00000 | 85.23256 | 85.93082 | 0.00000 | 17.00000 | 57.00000 | 126.00000 | 443.00000 |
| arrival_year | 36275.00000 | 2017.82043 | 0.38384 | 2017.00000 | 2018.00000 | 2018.00000 | 2018.00000 | 2018.00000 |
| arrival_month | 36275.00000 | 7.42365 | 3.06989 | 1.00000 | 5.00000 | 8.00000 | 10.00000 | 12.00000 |
| arrival_date | 36275.00000 | 15.59700 | 8.74045 | 1.00000 | 8.00000 | 16.00000 | 23.00000 | 31.00000 |
| repeated_guest | 36275.00000 | 0.02564 | 0.15805 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 |
| no_of_previous_cancellations | 36275.00000 | 0.02335 | 0.36833 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 13.00000 |
| no_of_previous_bookings_not_canceled | 36275.00000 | 0.15341 | 1.75417 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 58.00000 |
| avg_price_per_room | 36275.00000 | 103.42354 | 35.08942 | 0.00000 | 80.30000 | 99.45000 | 120.00000 | 540.00000 |
| no_of_special_requests | 36275.00000 | 0.61966 | 0.78624 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 5.00000 |
def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (15,10))
kde: whether to show the density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a triangle will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
histogram_boxplot(data, "lead_time")
histogram_boxplot(data, 'avg_price_per_room')
data[data["avg_price_per_room"] == 0]
| no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 63 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 2 | 2017 | 9 | 10 | Complementary | 0 | 0 | 0 | 0.00000 | 1 | Not_Canceled |
| 145 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 13 | 2018 | 6 | 1 | Complementary | 1 | 3 | 5 | 0.00000 | 1 | Not_Canceled |
| 209 | 1 | 0 | 0 | 0 | Meal Plan 1 | 0 | Room_Type 1 | 4 | 2018 | 2 | 27 | Complementary | 0 | 0 | 0 | 0.00000 | 1 | Not_Canceled |
| 266 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2017 | 8 | 12 | Complementary | 1 | 0 | 1 | 0.00000 | 1 | Not_Canceled |
| 267 | 1 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 4 | 2017 | 8 | 23 | Complementary | 0 | 0 | 0 | 0.00000 | 1 | Not_Canceled |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 35983 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 7 | 0 | 2018 | 6 | 7 | Complementary | 1 | 4 | 17 | 0.00000 | 1 | Not_Canceled |
| 36080 | 1 | 0 | 1 | 1 | Meal Plan 1 | 0 | Room_Type 7 | 0 | 2018 | 3 | 21 | Complementary | 1 | 3 | 15 | 0.00000 | 1 | Not_Canceled |
| 36114 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2018 | 3 | 2 | Online | 0 | 0 | 0 | 0.00000 | 0 | Not_Canceled |
| 36217 | 2 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 2 | 3 | 2017 | 8 | 9 | Online | 0 | 0 | 0 | 0.00000 | 2 | Not_Canceled |
| 36250 | 1 | 0 | 0 | 2 | Meal Plan 2 | 0 | Room_Type 1 | 6 | 2017 | 12 | 10 | Online | 0 | 0 | 0 | 0.00000 | 0 | Not_Canceled |
545 rows × 18 columns
data.loc[data["avg_price_per_room"] == 0, "market_segment_type"].value_counts()
Complementary 354 Online 191 Name: market_segment_type, dtype: int64
histogram_boxplot(data, 'no_of_previous_cancellations')
histogram_boxplot(data, 'no_of_previous_bookings_not_canceled')
# function to create labeled barplots
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 2, 6))
else:
plt.figure(figsize=(n + 2, 6))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n],
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
plt.show() # show the plot
labeled_barplot(data, "no_of_adults", perc=True)
labeled_barplot(data, 'no_of_children')
labeled_barplot(data, 'no_of_week_nights')
labeled_barplot(data, 'no_of_weekend_nights')
labeled_barplot(data, 'required_car_parking_space')
labeled_barplot(data, 'type_of_meal_plan')
labeled_barplot(data, 'room_type_reserved')
labeled_barplot(data, 'arrival_month')
labeled_barplot(data, 'market_segment_type')
labeled_barplot(data, 'no_of_special_requests')
labeled_barplot(data, 'booking_status')
Let's encode Canceled bookings to 1 and Not_Canceled as 0 for further analysis
data["booking_status"] = data["booking_status"].apply(
lambda x: 1 if x == "Canceled" else 0
)
data['booking_status']
0 0
1 0
2 1
3 1
4 1
..
36270 0
36271 1
36272 0
36273 1
36274 0
Name: booking_status, Length: 36275, dtype: int64
### function to plot distributions wrt target
def distribution_plot_wrt_target(data, predictor, target):
fig, axs = plt.subplots(2, 2, figsize=(12, 10))
target_uniq = data[target].unique()
axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
sns.histplot(
data=data[data[target] == target_uniq[0]],
x=predictor,
kde=True,
ax=axs[0, 0],
color="teal",
stat="density",
)
axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
sns.histplot(
data=data[data[target] == target_uniq[1]],
x=predictor,
kde=True,
ax=axs[0, 1],
color="orange",
stat="density",
)
axs[1, 0].set_title("Boxplot w.r.t target")
sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")
axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
sns.boxplot(
data=data,
x=target,
y=predictor,
ax=axs[1, 1],
showfliers=False,
palette="gist_rainbow",
)
plt.tight_layout()
plt.show()
def stacked_barplot(data, predictor, target):
"""
Print the category counts and plot a stacked bar chart
data: dataframe
predictor: independent variable
target: target variable
"""
count = data[predictor].nunique()
sorter = data[target].value_counts().index[-1]
tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
by=sorter, ascending=False
)
print(tab1)
print("-" * 120)
tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
by=sorter, ascending=False
)
tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
plt.legend(
loc="lower left", frameon=False,
)
plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
plt.show()
Hotel rates are dynamic and change according to demand and customer demographics. Let's see how prices vary across different market segments
plt.figure(figsize=(10, 6))
sns.boxplot(
data=data, x="market_segment_type", y="avg_price_per_room", palette="gist_rainbow"
)
plt.show()
Let's see how booking status varies across different market segments. Also, how average price per room impacts booking status
stacked_barplot(data, "market_segment_type", "booking_status")
booking_status 0 1 All market_segment_type All 24390 11885 36275 Online 14739 8475 23214 Offline 7375 3153 10528 Corporate 1797 220 2017 Aviation 88 37 125 Complementary 391 0 391 ------------------------------------------------------------------------------------------------------------------------
Many guests have special requirements when booking a hotel room. Let's see how it impacts cancellations
stacked_barplot(data, "no_of_special_requests", "booking_status")
booking_status 0 1 All no_of_special_requests All 24390 11885 36275 0 11232 8545 19777 1 8670 2703 11373 2 3727 637 4364 3 675 0 675 4 78 0 78 5 8 0 8 ------------------------------------------------------------------------------------------------------------------------
Let's see if the special requests made by the customers impacts the prices of a room
plt.figure(figsize=(10, 5))
sns.boxplot(data, x = 'no_of_special_requests', y = 'avg_price_per_room', showfliers = False) ## Complete the code to create boxplot for no of special requests and average price per room (excluding the outliers)
plt.show()
We saw earlier that there is a positive correlation between booking status and average price per room. Let's analyze it
distribution_plot_wrt_target(data, "avg_price_per_room", "booking_status")
There is a positive correlation between booking status and lead time also. Let's analyze it further
distribution_plot_wrt_target(data, "lead_time", "booking_status")
Generally people travel with their spouse and children for vacations or other activities. Let's create a new dataframe of the customers who traveled with their families and analyze the impact on booking status.
family_data = data[(data["no_of_children"] >= 0) & (data["no_of_adults"] > 1)]
family_data.shape
(28441, 18)
family_data["no_of_family_members"] = (
family_data["no_of_adults"] + family_data["no_of_children"]
)
stacked_barplot(family_data, 'no_of_family_members', 'booking_status');
booking_status 0 1 All no_of_family_members All 18456 9985 28441 2 15506 8213 23719 3 2425 1368 3793 4 514 398 912 5 10 5 15 11 0 1 1 12 1 0 1 ------------------------------------------------------------------------------------------------------------------------
Let's do a similar analysis for the customer who stay for at least a day at the hotel.
stay_data = data[(data["no_of_week_nights"] > 0) & (data["no_of_weekend_nights"] > 0)]
stay_data.shape
(17094, 18)
stay_data["total_days"] = (
stay_data["no_of_week_nights"] + stay_data["no_of_weekend_nights"]
)
stacked_barplot(stay_data, 'total_days', 'booking_status')
booking_status 0 1 All total_days All 10979 6115 17094 3 3689 2183 5872 4 2977 1387 4364 5 1593 738 2331 2 1301 639 1940 6 566 465 1031 7 590 383 973 8 100 79 179 10 51 58 109 9 58 53 111 14 5 27 32 15 5 26 31 13 3 15 18 12 9 15 24 11 24 15 39 20 3 8 11 19 1 5 6 16 1 5 6 17 1 4 5 18 0 3 3 21 1 3 4 22 0 2 2 23 1 1 2 24 0 1 1 ------------------------------------------------------------------------------------------------------------------------
Repeating guests are the guests who stay in the hotel often and are important to brand equity. Let's see what percentage of repeating guests cancel?
stacked_barplot(data, 'repeated_guest', 'booking_status')
booking_status 0 1 All repeated_guest All 24390 11885 36275 0 23476 11869 35345 1 914 16 930 ------------------------------------------------------------------------------------------------------------------------
sns.pairplot(data[['no_of_weekend_nights','no_of_week_nights','required_car_parking_space',
'lead_time','avg_price_per_room','no_of_special_requests','type_of_meal_plan',
'room_type_reserved','market_segment_type','booking_status']]);
Leading Questions:
1. What are the busiest months in the hotel?
labeled_barplot(data, 'arrival_month', perc=True, n=None)
Month 10 = October with 14.7% of the total booking for the year.
2. Which market segment do most of the guests come from?
labeled_barplot(data, 'market_segment_type', perc=True, n=None)
Online 23214 or 64% of the bookings come via the internet.
3. Hotel rates are dynamic and change according to demand and customer demographics. What are the differences in room prices in different market segments?
histogram_boxplot(data, 'avg_price_per_room')
# how many free rooms does the hotel give away?
data[data['avg_price_per_room']==0]
| no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 63 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 2 | 2017 | 9 | 10 | Complementary | 0 | 0 | 0 | 0.00000 | 1 | 0 |
| 145 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 13 | 2018 | 6 | 1 | Complementary | 1 | 3 | 5 | 0.00000 | 1 | 0 |
| 209 | 1 | 0 | 0 | 0 | Meal Plan 1 | 0 | Room_Type 1 | 4 | 2018 | 2 | 27 | Complementary | 0 | 0 | 0 | 0.00000 | 1 | 0 |
| 266 | 1 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2017 | 8 | 12 | Complementary | 1 | 0 | 1 | 0.00000 | 1 | 0 |
| 267 | 1 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 4 | 2017 | 8 | 23 | Complementary | 0 | 0 | 0 | 0.00000 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 35983 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 7 | 0 | 2018 | 6 | 7 | Complementary | 1 | 4 | 17 | 0.00000 | 1 | 0 |
| 36080 | 1 | 0 | 1 | 1 | Meal Plan 1 | 0 | Room_Type 7 | 0 | 2018 | 3 | 21 | Complementary | 1 | 3 | 15 | 0.00000 | 1 | 0 |
| 36114 | 1 | 0 | 0 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2018 | 3 | 2 | Online | 0 | 0 | 0 | 0.00000 | 0 | 0 |
| 36217 | 2 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 2 | 3 | 2017 | 8 | 9 | Online | 0 | 0 | 0 | 0.00000 | 2 | 0 |
| 36250 | 1 | 0 | 0 | 2 | Meal Plan 2 | 0 | Room_Type 1 | 6 | 2017 | 12 | 10 | Online | 0 | 0 | 0 | 0.00000 | 0 | 0 |
545 rows × 18 columns
data.loc[data['avg_price_per_room']==0, 'market_segment_type'].value_counts()
Complementary 354 Online 191 Name: market_segment_type, dtype: int64
# Calculating the 25th quantile
Q1 = data["avg_price_per_room"].quantile(0.25)
# Calculating the 75th quantile
Q3 = data["avg_price_per_room"].quantile(0.75) ## Complete the code to calculate 75th quantile for average price per room
# Calculating IQR
IQR = Q3 - Q1
# Calculating value of upper whisker
Upper_Whisker = Q3 + 1.5 * IQR
Upper_Whisker
179.55
# assigning the outliers the value of upper whisker
data.loc[data["avg_price_per_room"] >= 500, "avg_price_per_room"] = Upper_Whisker
sns.boxplot(data=data, y='avg_price_per_room' , x='market_segment_type')
<Axes: xlabel='market_segment_type', ylabel='avg_price_per_room'>
Online booking are the highest despite also having the highest amount of free rooms. Aviation, Offline, and Corporate are generally slightly lower priced with Corporate edging out for the lowest. Complimentary are of course free.
4. What percentage of bookings are canceled?
labeled_barplot(data, 'booking_status', perc=True, n=None)
About 1/3 (11885) of bookings are canceled in the sample data.
5. Repeating guests are the guests who stay in the hotel often and are important to brand equity. What percentage of repeating guests cancel?
stacked_barplot(data,'repeated_guest','booking_status')
booking_status 0 1 All repeated_guest All 24390 11885 36275 0 23476 11869 35345 1 914 16 930 ------------------------------------------------------------------------------------------------------------------------
Repeating guest rarely cancel (1.75%).
6. Many guests have special requirements when booking a hotel room. Do these requirements affect booking cancellation?
sns.catplot(data=data, y='no_of_special_requests', hue='booking_status', kind='count' )
<seaborn.axisgrid.FacetGrid at 0x7f07933fd490>
The absence of special request increases the likelihood of cancellation, the addition of special request begins to reduce the likelihood of cancellation at one and progressively reduces cancellation to Zero on the instance of a third request.
EDA Insights:
# Checking the missing values again
data.isnull().sum()
no_of_adults 0 no_of_children 0 no_of_weekend_nights 0 no_of_week_nights 0 type_of_meal_plan 0 required_car_parking_space 0 room_type_reserved 0 lead_time 0 arrival_year 0 arrival_month 0 arrival_date 0 market_segment_type 0 repeated_guest 0 no_of_previous_cancellations 0 no_of_previous_bookings_not_canceled 0 avg_price_per_room 0 no_of_special_requests 0 booking_status 0 dtype: int64
No missing value
# outlier detection using boxplot
numeric_columns = data.select_dtypes(include=np.number).columns.tolist()
# dropping booking_status
numeric_columns.remove("booking_status")
plt.figure(figsize=(15, 12))
for i, variable in enumerate(numeric_columns):
plt.subplot(4, 4, i + 1)
plt.boxplot(data[variable], whis=1.5)
plt.tight_layout()
plt.title(variable)
plt.show()
There are two heavy outlier columns, lead_time & avg_room_price. We have already treated the average room price value by treating with IQR. We can leave others to be as the treatment is not that necessary.
Both the cases are important as:
If we predict that a booking will not be canceled and the booking gets canceled then the hotel will lose resources and will have to bear additional costs of distribution channels.
If we predict that a booking will get canceled and the booking doesn't get canceled the hotel might not be able to provide satisfactory services to the customer by assuming that this booking will be canceled. This might damage the brand equity.
F1 Score to be maximized, greater the F1 score higher are the chances of minimizing False Negatives and False Positives. # defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
model, predictors, target, threshold=0.5
):
"""
Function to compute different metrics to check classification model performance
model: classifier
predictors: independent variables
target: dependent variable
threshold: threshold for classifying the observation as class 1
"""
# checking which probabilities are greater than threshold
pred_temp = model.predict(predictors) > threshold
# rounding off the above values to get classes
pred = np.round(pred_temp)
acc = accuracy_score(target, pred) # to compute Accuracy
recall = recall_score(target, pred) # to compute Recall
precision = precision_score(target, pred) # to compute Precision
f1 = f1_score(target, pred) # to compute F1-score
# creating a dataframe of metrics
df_perf = pd.DataFrame(
{"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
index=[0],
)
return df_perf
# defining a function to plot the confusion_matrix of a classification model
def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
"""
To plot the confusion_matrix with percentages
model: classifier
predictors: independent variables
target: dependent variable
threshold: threshold for classifying the observation as class 1
"""
y_pred = model.predict(predictors) > threshold
cm = confusion_matrix(target, y_pred)
labels = np.asarray(
[
["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
for item in cm.flatten()
]
).reshape(2, 2)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=labels, fmt="")
plt.ylabel("True label")
plt.xlabel("Predicted label")
X = data.drop(["booking_status"], axis=1) # dropping the booking_id column as it has just unique ids and of not much use
Y = data["booking_status"]
# adding constant
X = sm.add_constant(X)
X = pd.get_dummies(X, drop_first=True) # Getting dummies for categorical variables
# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1)
print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))
Shape of Training set : (25392, 28) Shape of test set : (10883, 28) Percentage of classes in training set: 0 0.67064 1 0.32936 Name: booking_status, dtype: float64 Percentage of classes in test set: 0 0.67638 1 0.32362 Name: booking_status, dtype: float64
# fitting logistic regression model
logit = sm.Logit(y_train, X_train.astype(float))
lg = logit.fit(
disp=False # setting disp=False will remove the information on number of iterations
)
print(lg.summary())
Logit Regression Results
==============================================================================
Dep. Variable: booking_status No. Observations: 25392
Model: Logit Df Residuals: 25364
Method: MLE Df Model: 27
Date: Fri, 14 Apr 2023 Pseudo R-squ.: 0.3292
Time: 22:13:31 Log-Likelihood: -10794.
converged: False LL-Null: -16091.
Covariance Type: nonrobust LLR p-value: 0.000
========================================================================================================
coef std err z P>|z| [0.025 0.975]
--------------------------------------------------------------------------------------------------------
const -924.1330 120.816 -7.649 0.000 -1160.928 -687.338
no_of_adults 0.1136 0.038 3.020 0.003 0.040 0.187
no_of_children 0.1561 0.057 2.727 0.006 0.044 0.268
no_of_weekend_nights 0.1067 0.020 5.394 0.000 0.068 0.145
no_of_week_nights 0.0398 0.012 3.236 0.001 0.016 0.064
required_car_parking_space -1.5942 0.138 -11.564 0.000 -1.864 -1.324
lead_time 0.0157 0.000 58.866 0.000 0.015 0.016
arrival_year 0.4567 0.060 7.629 0.000 0.339 0.574
arrival_month -0.0416 0.006 -6.436 0.000 -0.054 -0.029
arrival_date 0.0005 0.002 0.264 0.792 -0.003 0.004
repeated_guest -2.3465 0.617 -3.805 0.000 -3.555 -1.138
no_of_previous_cancellations 0.2663 0.086 3.108 0.002 0.098 0.434
no_of_previous_bookings_not_canceled -0.1727 0.153 -1.131 0.258 -0.472 0.127
avg_price_per_room 0.0188 0.001 25.444 0.000 0.017 0.020
no_of_special_requests -1.4690 0.030 -48.791 0.000 -1.528 -1.410
type_of_meal_plan_Meal Plan 2 0.1756 0.067 2.636 0.008 0.045 0.306
type_of_meal_plan_Meal Plan 3 17.4845 4247.878 0.004 0.997 -8308.203 8343.172
type_of_meal_plan_Not Selected 0.2783 0.053 5.247 0.000 0.174 0.382
room_type_reserved_Room_Type 2 -0.3607 0.131 -2.758 0.006 -0.617 -0.104
room_type_reserved_Room_Type 3 -0.0014 1.310 -0.001 0.999 -2.569 2.566
room_type_reserved_Room_Type 4 -0.2829 0.053 -5.320 0.000 -0.387 -0.179
room_type_reserved_Room_Type 5 -0.7187 0.209 -3.437 0.001 -1.128 -0.309
room_type_reserved_Room_Type 6 -0.9474 0.147 -6.447 0.000 -1.235 -0.659
room_type_reserved_Room_Type 7 -1.3992 0.293 -4.776 0.000 -1.973 -0.825
market_segment_type_Complementary -40.9223 6.23e+05 -6.56e-05 1.000 -1.22e+06 1.22e+06
market_segment_type_Corporate -1.1940 0.266 -4.489 0.000 -1.715 -0.673
market_segment_type_Offline -2.1948 0.255 -8.622 0.000 -2.694 -1.696
market_segment_type_Online -0.3996 0.251 -1.590 0.112 -0.892 0.093
========================================================================================================
# Looking out for training data performance
print("Training performance:")
model_performance_classification_statsmodels(lg, X_train, y_train)
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80596 | 0.63410 | 0.73961 | 0.68280 |
# we will define a function to check VIF
def checking_vif(predictors):
vif = pd.DataFrame()
vif["feature"] = predictors.columns
# calculating VIF for each feature
vif["VIF"] = [
variance_inflation_factor(predictors.values, i)
for i in range(len(predictors.columns))
]
return vif
# Checking vif values
checking_vif(X_train)
| feature | VIF | |
|---|---|---|
| 0 | const | 39491186.47744 |
| 1 | no_of_adults | 1.34849 |
| 2 | no_of_children | 1.97862 |
| 3 | no_of_weekend_nights | 1.06949 |
| 4 | no_of_week_nights | 1.09567 |
| 5 | required_car_parking_space | 1.03998 |
| 6 | lead_time | 1.39518 |
| 7 | arrival_year | 1.43167 |
| 8 | arrival_month | 1.27637 |
| 9 | arrival_date | 1.00674 |
| 10 | repeated_guest | 1.78361 |
| 11 | no_of_previous_cancellations | 1.39569 |
| 12 | no_of_previous_bookings_not_canceled | 1.65200 |
| 13 | avg_price_per_room | 2.06421 |
| 14 | no_of_special_requests | 1.24730 |
| 15 | type_of_meal_plan_Meal Plan 2 | 1.27325 |
| 16 | type_of_meal_plan_Meal Plan 3 | 1.02522 |
| 17 | type_of_meal_plan_Not Selected | 1.27252 |
| 18 | room_type_reserved_Room_Type 2 | 1.10151 |
| 19 | room_type_reserved_Room_Type 3 | 1.00330 |
| 20 | room_type_reserved_Room_Type 4 | 1.36261 |
| 21 | room_type_reserved_Room_Type 5 | 1.02797 |
| 22 | room_type_reserved_Room_Type 6 | 1.97490 |
| 23 | room_type_reserved_Room_Type 7 | 1.11559 |
| 24 | market_segment_type_Complementary | 4.50229 |
| 25 | market_segment_type_Corporate | 16.92846 |
| 26 | market_segment_type_Offline | 64.11425 |
| 27 | market_segment_type_Online | 71.17686 |
The above process can also be done manually by picking one variable at a time that has a high p-value, dropping it, and building a model again. But that might be a little tedious and using a loop will be more efficient.
# initial list of columns
cols = X_train.columns.tolist()
# setting an initial max p-value
max_p_value = 1
while len(cols) > 0:
# defining the train set
x_train_aux = X_train[cols]
# fitting the model
model = sm.Logit(y_train, x_train_aux).fit(disp=False)
# getting the p-values and the maximum p-value
p_values = model.pvalues
max_p_value = max(p_values)
# name of the variable with maximum p-value
feature_with_p_max = p_values.idxmax()
if max_p_value > 0.05:
cols.remove(feature_with_p_max)
else:
break
selected_features = cols
print(selected_features)
['const', 'no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'required_car_parking_space', 'lead_time', 'arrival_year', 'arrival_month', 'repeated_guest', 'no_of_previous_cancellations', 'avg_price_per_room', 'no_of_special_requests', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Corporate', 'market_segment_type_Offline']
X_train1 = X_train[selected_features]
X_test1 = X_test[selected_features]
logit1 = sm.Logit(y_train, X_train1.astype(float))
lg1 = logit1.fit(
disp=False # setting disp=False will remove the information on number of iterations
)
print(lg1.summary())
Logit Regression Results
==============================================================================
Dep. Variable: booking_status No. Observations: 25392
Model: Logit Df Residuals: 25370
Method: MLE Df Model: 21
Date: Fri, 14 Apr 2023 Pseudo R-squ.: 0.3282
Time: 22:13:36 Log-Likelihood: -10810.
converged: True LL-Null: -16091.
Covariance Type: nonrobust LLR p-value: 0.000
==================================================================================================
coef std err z P>|z| [0.025 0.975]
--------------------------------------------------------------------------------------------------
const -916.8647 120.456 -7.612 0.000 -1152.953 -680.776
no_of_adults 0.1087 0.037 2.916 0.004 0.036 0.182
no_of_children 0.1520 0.057 2.656 0.008 0.040 0.264
no_of_weekend_nights 0.1086 0.020 5.497 0.000 0.070 0.147
no_of_week_nights 0.0417 0.012 3.400 0.001 0.018 0.066
required_car_parking_space -1.5947 0.138 -11.564 0.000 -1.865 -1.324
lead_time 0.0157 0.000 59.216 0.000 0.015 0.016
arrival_year 0.4529 0.060 7.587 0.000 0.336 0.570
arrival_month -0.0425 0.006 -6.586 0.000 -0.055 -0.030
repeated_guest -2.7358 0.557 -4.914 0.000 -3.827 -1.645
no_of_previous_cancellations 0.2288 0.077 2.982 0.003 0.078 0.379
avg_price_per_room 0.0192 0.001 26.382 0.000 0.018 0.021
no_of_special_requests -1.4700 0.030 -48.893 0.000 -1.529 -1.411
type_of_meal_plan_Meal Plan 2 0.1643 0.067 2.470 0.014 0.034 0.295
type_of_meal_plan_Not Selected 0.2860 0.053 5.407 0.000 0.182 0.390
room_type_reserved_Room_Type 2 -0.3557 0.131 -2.722 0.006 -0.612 -0.100
room_type_reserved_Room_Type 4 -0.2833 0.053 -5.344 0.000 -0.387 -0.179
room_type_reserved_Room_Type 5 -0.7362 0.208 -3.534 0.000 -1.144 -0.328
room_type_reserved_Room_Type 6 -0.9667 0.147 -6.584 0.000 -1.254 -0.679
room_type_reserved_Room_Type 7 -1.4338 0.293 -4.901 0.000 -2.007 -0.860
market_segment_type_Corporate -0.7927 0.103 -7.710 0.000 -0.994 -0.591
market_segment_type_Offline -1.7855 0.052 -34.377 0.000 -1.887 -1.684
==================================================================================================
All the p-values are less than 0.05, so we will stop dropping the columns and check for the model performance.
print("Training performance:")
model_performance_classification_statsmodels(lg1,X_train1,y_train)
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80537 | 0.63243 | 0.73900 | 0.68157 |
# converting coefficients to odds
odds = np.exp(lg1.params)
# finding the percentage change
perc_change_odds = (np.exp(lg1.params) - 1) * 100
# removing limit from number of columns to display
pd.set_option("display.max_columns", None)
# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train1.columns).T
| const | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | required_car_parking_space | lead_time | arrival_year | arrival_month | repeated_guest | no_of_previous_cancellations | avg_price_per_room | no_of_special_requests | type_of_meal_plan_Meal Plan 2 | type_of_meal_plan_Not Selected | room_type_reserved_Room_Type 2 | room_type_reserved_Room_Type 4 | room_type_reserved_Room_Type 5 | room_type_reserved_Room_Type 6 | room_type_reserved_Room_Type 7 | market_segment_type_Corporate | market_segment_type_Offline | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Odds | 0.00000 | 1.11487 | 1.16411 | 1.11466 | 1.04260 | 0.20298 | 1.01583 | 1.57291 | 0.95841 | 0.06484 | 1.25705 | 1.01937 | 0.22993 | 1.17855 | 1.33103 | 0.70069 | 0.75327 | 0.47893 | 0.38034 | 0.23840 | 0.45262 | 0.16771 |
| Change_odd% | -100.00000 | 11.48701 | 16.41072 | 11.46616 | 4.25996 | -79.70242 | 1.58342 | 57.29054 | -4.15858 | -93.51608 | 25.70460 | 1.93739 | -77.00672 | 17.85489 | 33.10347 | -29.93109 | -24.67346 | -52.10654 | -61.96631 | -76.15999 | -54.73798 | -83.22899 |
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train1, y_train)
print("Training performance:")
log_reg_model_train_perf = model_performance_classification_statsmodels(lg1, X_train1, y_train) ## Complete the code to check performance on X_train1 and y_train
log_reg_model_train_perf
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80537 | 0.63243 | 0.73900 | 0.68157 |
logit_roc_auc_train = roc_auc_score(y_train, lg1.predict(X_train1))
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()
# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))
optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)
0.369603791589294
# creating confusion matrix
confusion_matrix_statsmodels(
lg1,X_train1,y_train
)
# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
lg1, X_train1, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.79257 | 0.73634 | 0.66790 | 0.70045 |
y_scores = lg1.predict(X_train1)
prec, rec, tre = precision_recall_curve(y_train, y_scores,)
def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
plt.plot(thresholds, precisions[:-1], "b--", label="precision")
plt.plot(thresholds, recalls[:-1], "g--", label="recall")
plt.xlabel("Threshold")
plt.legend(loc="upper left")
plt.ylim([0, 1])
plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()
# setting the threshold
optimal_threshold_curve = 0.42
# creating confusion matrix
confusion_matrix_statsmodels(
lg1, X_train1,y_train, threshold=optimal_threshold_curve
)
log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
lg1, X_train1, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80128 | 0.69927 | 0.69794 | 0.69860 |
Using model with default threshold
# creating confusion matrix
confusion_matrix_statsmodels(lg1,X_test1, y_test)
log_reg_model_test_perf = model_performance_classification_statsmodels(lg1, X_test1, y_test)
print("Test performance:")
log_reg_model_test_perf
Test performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80465 | 0.63089 | 0.72900 | 0.67641 |
logit_roc_auc_train = roc_auc_score(y_test, lg1.predict(X_test1))
fpr, tpr, thresholds = roc_curve(y_test, lg1.predict(X_test1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()
Using model with threshold=0.37
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test, threshold = optimal_threshold_auc_roc)
# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
lg1, X_test1, y_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc
Test performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.79555 | 0.73964 | 0.66573 | 0.70074 |
Using model with threshold = 0.42
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test)
log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
lg1, X_test1, y_test, threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve
Test performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80364 | 0.70386 | 0.69381 | 0.69880 |
# training performance comparison
models_train_comp_df = pd.concat(
[
log_reg_model_train_perf.T,
log_reg_model_train_perf_threshold_auc_roc.T,
log_reg_model_train_perf_threshold_curve.T,
],
axis=1,
)
models_train_comp_df.columns = [
"Logistic Regression-default Threshold",
"Logistic Regression-0.37 Threshold",
"Logistic Regression-0.42 Threshold",
]
print("Training performance comparison:")
models_train_comp_df
Training performance comparison:
| Logistic Regression-default Threshold | Logistic Regression-0.37 Threshold | Logistic Regression-0.42 Threshold | |
|---|---|---|---|
| Accuracy | 0.80537 | 0.79257 | 0.80128 |
| Recall | 0.63243 | 0.73634 | 0.69927 |
| Precision | 0.73900 | 0.66790 | 0.69794 |
| F1 | 0.68157 | 0.70045 | 0.69860 |
# test performance comparison
models_test_comp_df = pd.concat(
[
log_reg_model_test_perf.T,
log_reg_model_test_perf_threshold_auc_roc.T,
log_reg_model_test_perf_threshold_curve.T,
],
axis=1,
)
models_test_comp_df.columns = [
"Logistic Regression-default Threshold",
"Logistic Regression-0.37 Threshold",
"Logistic Regression-0.42 Threshold",
]
print("Test performance comparison:")
models_test_comp_df
Test performance comparison:
| Logistic Regression-default Threshold | Logistic Regression-0.37 Threshold | Logistic Regression-0.42 Threshold | |
|---|---|---|---|
| Accuracy | 0.80465 | 0.79555 | 0.80364 |
| Recall | 0.63089 | 0.73964 | 0.70386 |
| Precision | 0.72900 | 0.66573 | 0.69381 |
| F1 | 0.67641 | 0.70074 | 0.69880 |
We see that the model with threshold 0.37 has the best results as the accuracy is better and F1 value is equivalent to almost highest out of three. So we can chose that model. But we still can look out for decision tree as the result does not seem to be very satisfactory.
X = data.drop(["booking_status"], axis=1)
Y = data["booking_status"]
X = pd.get_dummies(X, drop_first=True)
# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1)
print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))
Shape of Training set : (25392, 27) Shape of test set : (10883, 27) Percentage of classes in training set: 0 0.67064 1 0.32936 Name: booking_status, dtype: float64 Percentage of classes in test set: 0 0.67638 1 0.32362 Name: booking_status, dtype: float64
# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
"""
Function to compute different metrics to check classification model performance
model: classifier
predictors: independent variables
target: dependent variable
"""
# predicting using the independent variables
pred = model.predict(predictors)
acc = accuracy_score(target, pred) # to compute Accuracy
recall = recall_score(target, pred) # to compute Recall
precision = precision_score(target, pred) # to compute Precision
f1 = f1_score(target, pred) # to compute F1-score
# creating a dataframe of metrics
df_perf = pd.DataFrame(
{"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
index=[0],
)
return df_perf
def confusion_matrix_sklearn(model, predictors, target):
"""
To plot the confusion_matrix with percentages
model: classifier
predictors: independent variables
target: dependent variable
"""
y_pred = model.predict(predictors)
cm = confusion_matrix(target, y_pred)
labels = np.asarray(
[
["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
for item in cm.flatten()
]
).reshape(2, 2)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=labels, fmt="")
plt.ylabel("True label")
plt.xlabel("Predicted label")
# Building a model
model = DecisionTreeClassifier(criterion="gini", random_state=1)
model.fit(X_train, y_train)
DecisionTreeClassifier(random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(random_state=1)
# Making confusion matrix for training data
confusion_matrix_sklearn(model, X_train, y_train)
# Looking at the values
decision_tree_perf_train = model_performance_classification_sklearn(
model, X_train, y_train
)
decision_tree_perf_train
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.99421 | 0.98661 | 0.99578 | 0.99117 |
confusion_matrix_sklearn(model, X_test, y_test)
decision_tree_perf_test = model_performance_classification_sklearn(model, X_train, y_train) ## checking performance on test set
decision_tree_perf_test
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.99421 | 0.98661 | 0.99578 | 0.99117 |
Before pruning the tree let's check the important features.
feature_names = list(X_train.columns) # shows the importance of every column in the data in that model.
importances = model.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
Pre-Pruning
# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1, class_weight="balanced")
# Grid of parameters to choose from
parameters = {
"max_depth": np.arange(2, 7, 2),
"max_leaf_nodes": [50, 75, 150, 250],
"min_samples_split": [10, 30, 50, 70],
}
# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(f1_score)
# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)
# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_
# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)
DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=50,
min_samples_split=10, random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=50,
min_samples_split=10, random_state=1)confusion_matrix_sklearn(model, X_train, y_train) ##code to create confusion matrix for train data
decision_tree_tune_perf_train = model_performance_classification_sklearn(model, X_train, y_train) ## Complete the code to check performance on train set
decision_tree_tune_perf_train
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.99421 | 0.98661 | 0.99578 | 0.99117 |
confusion_matrix_sklearn(model, X_test, y_test) ##Code to create confusion matrix for test data
decision_tree_tune_perf_test = model_performance_classification_sklearn(model, X_test, y_test) ##Code to check performance on test set
decision_tree_tune_perf_test
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.87118 | 0.81175 | 0.79461 | 0.80309 |
plt.figure(figsize=(20, 10))
out = tree.plot_tree(
estimator,
feature_names=feature_names,
filled=True,
fontsize=9,
node_ids=False,
class_names=None,
)
# below code will add arrows to the decision tree split if they are missing
for o in out:
arrow = o.arrow_patch
if arrow is not None:
arrow.set_edgecolor("black")
arrow.set_linewidth(1)
plt.show()
# Text report showing the rules of a decision tree -
print(tree.export_text(estimator, feature_names=feature_names, show_weights=True))
|--- lead_time <= 151.50 | |--- no_of_special_requests <= 0.50 | | |--- market_segment_type_Online <= 0.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_weekend_nights <= 0.50 | | | | | |--- avg_price_per_room <= 196.50 | | | | | | |--- weights: [1736.39, 133.59] class: 0 | | | | | |--- avg_price_per_room > 196.50 | | | | | | |--- weights: [0.75, 24.29] class: 1 | | | | |--- no_of_weekend_nights > 0.50 | | | | | |--- lead_time <= 68.50 | | | | | | |--- weights: [960.27, 223.16] class: 0 | | | | | |--- lead_time > 68.50 | | | | | | |--- weights: [129.73, 160.92] class: 1 | | | |--- lead_time > 90.50 | | | | |--- lead_time <= 117.50 | | | | | |--- avg_price_per_room <= 93.58 | | | | | | |--- weights: [214.72, 227.72] class: 1 | | | | | |--- avg_price_per_room > 93.58 | | | | | | |--- weights: [82.76, 285.41] class: 1 | | | | |--- lead_time > 117.50 | | | | | |--- no_of_week_nights <= 1.50 | | | | | | |--- weights: [87.23, 81.98] class: 0 | | | | | |--- no_of_week_nights > 1.50 | | | | | | |--- weights: [228.14, 48.58] class: 0 | | |--- market_segment_type_Online > 0.50 | | | |--- lead_time <= 13.50 | | | | |--- avg_price_per_room <= 99.44 | | | | | |--- arrival_month <= 1.50 | | | | | | |--- weights: [92.45, 0.00] class: 0 | | | | | |--- arrival_month > 1.50 | | | | | | |--- weights: [363.83, 132.08] class: 0 | | | | |--- avg_price_per_room > 99.44 | | | | | |--- lead_time <= 3.50 | | | | | | |--- weights: [219.94, 85.01] class: 0 | | | | | |--- lead_time > 3.50 | | | | | | |--- weights: [132.71, 280.85] class: 1 | | | |--- lead_time > 13.50 | | | | |--- required_car_parking_space <= 0.50 | | | | | |--- avg_price_per_room <= 71.92 | | | | | | |--- weights: [158.80, 159.40] class: 1 | | | | | |--- avg_price_per_room > 71.92 | | | | | | |--- weights: [850.67, 3543.28] class: 1 | | | | |--- required_car_parking_space > 0.50 | | | | | |--- weights: [48.46, 1.52] class: 0 | |--- no_of_special_requests > 0.50 | | |--- no_of_special_requests <= 1.50 | | | |--- market_segment_type_Online <= 0.50 | | | | |--- lead_time <= 102.50 | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | |--- weights: [697.09, 9.11] class: 0 | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | |--- weights: [15.66, 9.11] class: 0 | | | | |--- lead_time > 102.50 | | | | | |--- no_of_week_nights <= 2.50 | | | | | | |--- weights: [32.06, 19.74] class: 0 | | | | | |--- no_of_week_nights > 2.50 | | | | | | |--- weights: [44.73, 3.04] class: 0 | | | |--- market_segment_type_Online > 0.50 | | | | |--- lead_time <= 8.50 | | | | | |--- lead_time <= 4.50 | | | | | | |--- weights: [498.03, 44.03] class: 0 | | | | | |--- lead_time > 4.50 | | | | | | |--- weights: [258.71, 63.76] class: 0 | | | | |--- lead_time > 8.50 | | | | | |--- required_car_parking_space <= 0.50 | | | | | | |--- weights: [2512.51, 1451.32] class: 0 | | | | | |--- required_car_parking_space > 0.50 | | | | | | |--- weights: [134.20, 1.52] class: 0 | | |--- no_of_special_requests > 1.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_week_nights <= 3.50 | | | | | |--- weights: [1585.04, 0.00] class: 0 | | | | |--- no_of_week_nights > 3.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- weights: [180.42, 57.69] class: 0 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [52.19, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- no_of_special_requests <= 2.50 | | | | | |--- arrival_month <= 8.50 | | | | | | |--- weights: [184.90, 56.17] class: 0 | | | | | |--- arrival_month > 8.50 | | | | | | |--- weights: [106.61, 106.27] class: 0 | | | | |--- no_of_special_requests > 2.50 | | | | | |--- weights: [67.10, 0.00] class: 0 |--- lead_time > 151.50 | |--- avg_price_per_room <= 100.04 | | |--- no_of_special_requests <= 0.50 | | | |--- no_of_adults <= 1.50 | | | | |--- market_segment_type_Online <= 0.50 | | | | | |--- lead_time <= 163.50 | | | | | | |--- weights: [3.73, 24.29] class: 1 | | | | | |--- lead_time > 163.50 | | | | | | |--- weights: [257.96, 62.24] class: 0 | | | | |--- market_segment_type_Online > 0.50 | | | | | |--- avg_price_per_room <= 2.50 | | | | | | |--- weights: [8.95, 3.04] class: 0 | | | | | |--- avg_price_per_room > 2.50 | | | | | | |--- weights: [0.75, 97.16] class: 1 | | | |--- no_of_adults > 1.50 | | | | |--- avg_price_per_room <= 82.47 | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | |--- weights: [2.98, 282.37] class: 1 | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | |--- weights: [213.97, 385.60] class: 1 | | | | |--- avg_price_per_room > 82.47 | | | | | |--- no_of_adults <= 2.50 | | | | | | |--- weights: [23.86, 1030.80] class: 1 | | | | | |--- no_of_adults > 2.50 | | | | | | |--- weights: [5.22, 0.00] class: 0 | | |--- no_of_special_requests > 0.50 | | | |--- no_of_weekend_nights <= 0.50 | | | | |--- lead_time <= 180.50 | | | | | |--- lead_time <= 159.50 | | | | | | |--- weights: [7.46, 7.59] class: 1 | | | | | |--- lead_time > 159.50 | | | | | | |--- weights: [37.28, 4.55] class: 0 | | | | |--- lead_time > 180.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- weights: [20.13, 212.54] class: 1 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [8.95, 0.00] class: 0 | | | |--- no_of_weekend_nights > 0.50 | | | | |--- market_segment_type_Offline <= 0.50 | | | | | |--- arrival_month <= 11.50 | | | | | | |--- weights: [231.12, 110.82] class: 0 | | | | | |--- arrival_month > 11.50 | | | | | | |--- weights: [19.38, 34.92] class: 1 | | | | |--- market_segment_type_Offline > 0.50 | | | | | |--- lead_time <= 348.50 | | | | | | |--- weights: [106.61, 3.04] class: 0 | | | | | |--- lead_time > 348.50 | | | | | | |--- weights: [5.96, 4.55] class: 0 | |--- avg_price_per_room > 100.04 | | |--- arrival_month <= 11.50 | | | |--- no_of_special_requests <= 2.50 | | | | |--- weights: [0.00, 3200.19] class: 1 | | | |--- no_of_special_requests > 2.50 | | | | |--- weights: [23.11, 0.00] class: 0 | | |--- arrival_month > 11.50 | | | |--- no_of_special_requests <= 0.50 | | | | |--- weights: [35.04, 0.00] class: 0 | | | |--- no_of_special_requests > 0.50 | | | | |--- arrival_date <= 24.50 | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | |--- arrival_date > 24.50 | | | | | |--- weights: [3.73, 22.77] class: 1
# importance of features in the tree building
importances = estimator.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
Cost Complexity Pruning
clf = DecisionTreeClassifier(random_state=1, class_weight="balanced")
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = abs(path.ccp_alphas), path.impurities
pd.DataFrame(path)
| ccp_alphas | impurities | |
|---|---|---|
| 0 | 0.00000 | 0.00838 |
| 1 | 0.00000 | 0.00838 |
| 2 | 0.00000 | 0.00838 |
| 3 | 0.00000 | 0.00838 |
| 4 | 0.00000 | 0.00838 |
| ... | ... | ... |
| 1853 | 0.00890 | 0.32806 |
| 1854 | 0.00980 | 0.33786 |
| 1855 | 0.01272 | 0.35058 |
| 1856 | 0.03412 | 0.41882 |
| 1857 | 0.08118 | 0.50000 |
1858 rows × 2 columns
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()
Next, we train a decision tree using effective alphas. The last value
in ccp_alphas is the alpha value that prunes the whole tree,
leaving the tree, clfs[-1], with one node.
clfs = []
for ccp_alpha in ccp_alphas:
clf = DecisionTreeClassifier(
random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
)
clf.fit(X_train, y_train) #code to fit decision tree on training data
clfs.append(clf)
print(
"Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
clfs[-1].tree_.node_count, ccp_alphas[-1]
)
)
Number of nodes in the last tree is: 1 with ccp_alpha: 0.08117914389136915
For the remainder, we remove the last element in
clfs and ccp_alphas, because it is the trivial tree with only one
node. Here we show that the number of nodes and tree depth decreases as alpha
increases.
f1_train = []
for clf in clfs:
pred_train = clf.predict(X_train)
values_train = f1_score(y_train, pred_train)
f1_train.append(values_train)
f1_test = []
for clf in clfs:
pred_test = clf.predict(X_test)
values_test = f1_score(y_test, pred_test)
f1_test.append(values_test)
fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("F1 Score")
ax.set_title("F1 Score vs alpha for training and testing sets")
ax.plot(ccp_alphas, f1_train, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, f1_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()
index_best_model = np.argmax(f1_test)
best_model = clfs[index_best_model]
print(best_model)
DecisionTreeClassifier(ccp_alpha=0.00012267633155167048,
class_weight='balanced', random_state=1)
confusion_matrix_sklearn(best_model, X_train, y_train)
decision_tree_post_perf_train = model_performance_classification_sklearn(
best_model, X_train, y_train
)
decision_tree_post_perf_train
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.89993 | 0.90291 | 0.81369 | 0.85598 |
confusion_matrix_sklearn(best_model, X_test, y_test)
decision_tree_post_test = model_performance_classification_sklearn(
best_model, X_test, y_test
) ##Code to check performance of test set on best model
decision_tree_post_test
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.86943 | 0.85662 | 0.76710 | 0.80939 |
plt.figure(figsize=(20, 10))
out = tree.plot_tree(
best_model,
feature_names=feature_names,
filled=True,
fontsize=9,
node_ids=False,
class_names=None,
)
for o in out:
arrow = o.arrow_patch
if arrow is not None:
arrow.set_edgecolor("black")
arrow.set_linewidth(1)
plt.show()
# Text report showing the rules of a decision tree -
print(tree.export_text(best_model, feature_names=feature_names, show_weights=True))
|--- lead_time <= 151.50 | |--- no_of_special_requests <= 0.50 | | |--- market_segment_type_Online <= 0.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_weekend_nights <= 0.50 | | | | | |--- avg_price_per_room <= 196.50 | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | |--- lead_time <= 16.50 | | | | | | | | |--- avg_price_per_room <= 68.50 | | | | | | | | | |--- weights: [207.26, 10.63] class: 0 | | | | | | | | |--- avg_price_per_room > 68.50 | | | | | | | | | |--- arrival_date <= 29.50 | | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | |--- arrival_date > 29.50 | | | | | | | | | | |--- weights: [2.24, 7.59] class: 1 | | | | | | | |--- lead_time > 16.50 | | | | | | | | |--- avg_price_per_room <= 135.00 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- no_of_previous_bookings_not_canceled <= 0.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- no_of_previous_bookings_not_canceled > 0.50 | | | | | | | | | | | |--- weights: [11.18, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- weights: [21.62, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 135.00 | | | | | | | | | |--- weights: [0.00, 12.14] class: 1 | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | |--- weights: [1199.59, 1.52] class: 0 | | | | | |--- avg_price_per_room > 196.50 | | | | | | |--- weights: [0.75, 24.29] class: 1 | | | | |--- no_of_weekend_nights > 0.50 | | | | | |--- lead_time <= 68.50 | | | | | | |--- arrival_month <= 9.50 | | | | | | | |--- avg_price_per_room <= 63.29 | | | | | | | | |--- arrival_date <= 20.50 | | | | | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | | | | | |--- weights: [41.75, 0.00] class: 0 | | | | | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | | | | | |--- weights: [0.75, 3.04] class: 1 | | | | | | | | |--- arrival_date > 20.50 | | | | | | | | | |--- avg_price_per_room <= 59.75 | | | | | | | | | | |--- arrival_date <= 23.50 | | | | | | | | | | | |--- weights: [1.49, 12.14] class: 1 | | | | | | | | | | |--- arrival_date > 23.50 | | | | | | | | | | | |--- weights: [14.91, 1.52] class: 0 | | | | | | | | | |--- avg_price_per_room > 59.75 | | | | | | | | | | |--- lead_time <= 44.00 | | | | | | | | | | | |--- weights: [0.75, 59.21] class: 1 | | | | | | | | | | |--- lead_time > 44.00 | | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | |--- avg_price_per_room > 63.29 | | | | | | | | |--- no_of_weekend_nights <= 3.50 | | | | | | | | | |--- lead_time <= 59.50 | | | | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_month > 7.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- lead_time > 59.50 | | | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | | | |--- weights: [20.13, 0.00] class: 0 | | | | | | | | |--- no_of_weekend_nights > 3.50 | | | | | | | | | |--- weights: [0.75, 15.18] class: 1 | | | | | | |--- arrival_month > 9.50 | | | | | | | |--- weights: [413.04, 27.33] class: 0 | | | | | |--- lead_time > 68.50 | | | | | | |--- avg_price_per_room <= 99.98 | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | |--- avg_price_per_room <= 62.50 | | | | | | | | | |--- weights: [15.66, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 62.50 | | | | | | | | | |--- avg_price_per_room <= 80.38 | | | | | | | | | | |--- lead_time <= 81.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- lead_time > 81.50 | | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | | | |--- avg_price_per_room > 80.38 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | |--- arrival_month > 3.50 | | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | | |--- weights: [55.17, 3.04] class: 0 | | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | | |--- lead_time <= 73.50 | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | | |--- lead_time > 73.50 | | | | | | | | | | |--- weights: [21.62, 4.55] class: 0 | | | | | | |--- avg_price_per_room > 99.98 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- weights: [8.95, 0.00] class: 0 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- avg_price_per_room <= 132.43 | | | | | | | | | |--- weights: [9.69, 122.97] class: 1 | | | | | | | | |--- avg_price_per_room > 132.43 | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- lead_time <= 117.50 | | | | | |--- avg_price_per_room <= 93.58 | | | | | | |--- avg_price_per_room <= 75.07 | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | |--- avg_price_per_room <= 58.75 | | | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 58.75 | | | | | | | | | |--- no_of_previous_cancellations <= 0.50 | | | | | | | | | | |--- arrival_month <= 4.50 | | | | | | | | | | | |--- weights: [2.24, 118.41] class: 1 | | | | | | | | | | |--- arrival_month > 4.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- no_of_previous_cancellations > 0.50 | | | | | | | | | | |--- weights: [4.47, 0.00] class: 0 | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | |--- arrival_date <= 11.50 | | | | | | | | | |--- weights: [31.31, 0.00] class: 0 | | | | | | | | |--- arrival_date > 11.50 | | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | | |--- weights: [23.11, 6.07] class: 0 | | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | | |--- weights: [5.96, 9.11] class: 1 | | | | | | |--- avg_price_per_room > 75.07 | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | |--- weights: [59.64, 3.04] class: 0 | | | | | | | |--- arrival_month > 3.50 | | | | | | | | |--- arrival_month <= 4.50 | | | | | | | | | |--- weights: [1.49, 16.70] class: 1 | | | | | | | | |--- arrival_month > 4.50 | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | |--- avg_price_per_room <= 86.00 | | | | | | | | | | | |--- weights: [2.24, 16.70] class: 1 | | | | | | | | | | |--- avg_price_per_room > 86.00 | | | | | | | | | | | |--- weights: [8.95, 3.04] class: 0 | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | |--- arrival_date <= 22.50 | | | | | | | | | | | |--- weights: [44.73, 4.55] class: 0 | | | | | | | | | | |--- arrival_date > 22.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | |--- avg_price_per_room > 93.58 | | | | | | |--- arrival_date <= 11.50 | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | |--- weights: [16.40, 39.47] class: 1 | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | |--- weights: [20.13, 6.07] class: 0 | | | | | | |--- arrival_date > 11.50 | | | | | | | |--- avg_price_per_room <= 102.09 | | | | | | | | |--- weights: [5.22, 144.22] class: 1 | | | | | | | |--- avg_price_per_room > 102.09 | | | | | | | | |--- avg_price_per_room <= 109.50 | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | |--- weights: [0.75, 16.70] class: 1 | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | |--- weights: [33.55, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 109.50 | | | | | | | | | |--- avg_price_per_room <= 124.25 | | | | | | | | | | |--- weights: [2.98, 75.91] class: 1 | | | | | | | | | |--- avg_price_per_room > 124.25 | | | | | | | | | | |--- weights: [3.73, 3.04] class: 0 | | | | |--- lead_time > 117.50 | | | | | |--- no_of_week_nights <= 1.50 | | | | | | |--- arrival_date <= 7.50 | | | | | | | |--- weights: [38.02, 0.00] class: 0 | | | | | | |--- arrival_date > 7.50 | | | | | | | |--- avg_price_per_room <= 93.58 | | | | | | | | |--- avg_price_per_room <= 65.38 | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | |--- avg_price_per_room > 65.38 | | | | | | | | | |--- weights: [24.60, 3.04] class: 0 | | | | | | | |--- avg_price_per_room > 93.58 | | | | | | | | |--- arrival_date <= 28.00 | | | | | | | | | |--- weights: [14.91, 72.87] class: 1 | | | | | | | | |--- arrival_date > 28.00 | | | | | | | | | |--- weights: [9.69, 1.52] class: 0 | | | | | |--- no_of_week_nights > 1.50 | | | | | | |--- no_of_adults <= 1.50 | | | | | | | |--- weights: [84.25, 0.00] class: 0 | | | | | | |--- no_of_adults > 1.50 | | | | | | | |--- lead_time <= 125.50 | | | | | | | | |--- avg_price_per_room <= 90.85 | | | | | | | | | |--- avg_price_per_room <= 87.50 | | | | | | | | | | |--- weights: [13.42, 13.66] class: 1 | | | | | | | | | |--- avg_price_per_room > 87.50 | | | | | | | | | | |--- weights: [0.00, 15.18] class: 1 | | | | | | | | |--- avg_price_per_room > 90.85 | | | | | | | | | |--- weights: [10.44, 0.00] class: 0 | | | | | | | |--- lead_time > 125.50 | | | | | | | | |--- arrival_date <= 19.50 | | | | | | | | | |--- weights: [58.15, 18.22] class: 0 | | | | | | | | |--- arrival_date > 19.50 | | | | | | | | | |--- weights: [61.88, 1.52] class: 0 | | |--- market_segment_type_Online > 0.50 | | | |--- lead_time <= 13.50 | | | | |--- avg_price_per_room <= 99.44 | | | | | |--- arrival_month <= 1.50 | | | | | | |--- weights: [92.45, 0.00] class: 0 | | | | | |--- arrival_month > 1.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | |--- avg_price_per_room <= 70.05 | | | | | | | | | |--- weights: [31.31, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 70.05 | | | | | | | | | |--- lead_time <= 5.50 | | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | | |--- weights: [38.77, 1.52] class: 0 | | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | |--- lead_time > 5.50 | | | | | | | | | | |--- arrival_date <= 3.50 | | | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 3.50 | | | | | | | | | | | |--- weights: [34.30, 40.99] class: 1 | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | |--- weights: [0.00, 19.74] class: 1 | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | |--- lead_time <= 2.50 | | | | | | | | | | |--- avg_price_per_room <= 74.21 | | | | | | | | | | | |--- weights: [0.75, 3.04] class: 1 | | | | | | | | | | |--- avg_price_per_room > 74.21 | | | | | | | | | | | |--- weights: [9.69, 0.00] class: 0 | | | | | | | | | |--- lead_time > 2.50 | | | | | | | | | | |--- weights: [4.47, 10.63] class: 1 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | |--- weights: [155.07, 6.07] class: 0 | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- weights: [3.73, 10.63] class: 1 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [7.46, 0.00] class: 0 | | | | |--- avg_price_per_room > 99.44 | | | | | |--- lead_time <= 3.50 | | | | | | |--- avg_price_per_room <= 202.67 | | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | |--- weights: [63.37, 30.36] class: 0 | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | |--- arrival_date <= 20.50 | | | | | | | | | | |--- weights: [115.56, 12.14] class: 0 | | | | | | | | | |--- arrival_date > 20.50 | | | | | | | | | | |--- arrival_date <= 24.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_date > 24.50 | | | | | | | | | | | |--- weights: [28.33, 3.04] class: 0 | | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | | |--- weights: [0.00, 6.07] class: 1 | | | | | | |--- avg_price_per_room > 202.67 | | | | | | | |--- weights: [0.75, 22.77] class: 1 | | | | | |--- lead_time > 3.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- avg_price_per_room <= 119.25 | | | | | | | | |--- avg_price_per_room <= 118.50 | | | | | | | | | |--- weights: [18.64, 59.21] class: 1 | | | | | | | | |--- avg_price_per_room > 118.50 | | | | | | | | | |--- weights: [8.20, 1.52] class: 0 | | | | | | | |--- avg_price_per_room > 119.25 | | | | | | | | |--- weights: [34.30, 171.55] class: 1 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- weights: [26.09, 1.52] class: 0 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- arrival_date <= 14.00 | | | | | | | | | | |--- weights: [9.69, 36.43] class: 1 | | | | | | | | | |--- arrival_date > 14.00 | | | | | | | | | | |--- avg_price_per_room <= 208.67 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- avg_price_per_room > 208.67 | | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [15.66, 0.00] class: 0 | | | |--- lead_time > 13.50 | | | | |--- required_car_parking_space <= 0.50 | | | | | |--- avg_price_per_room <= 71.92 | | | | | | |--- avg_price_per_room <= 59.43 | | | | | | | |--- lead_time <= 84.50 | | | | | | | | |--- weights: [50.70, 7.59] class: 0 | | | | | | | |--- lead_time > 84.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- arrival_date <= 27.00 | | | | | | | | | | |--- lead_time <= 131.50 | | | | | | | | | | | |--- weights: [0.75, 15.18] class: 1 | | | | | | | | | | |--- lead_time > 131.50 | | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | | | |--- arrival_date > 27.00 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- weights: [10.44, 0.00] class: 0 | | | | | | |--- avg_price_per_room > 59.43 | | | | | | | |--- lead_time <= 25.50 | | | | | | | | |--- weights: [20.88, 6.07] class: 0 | | | | | | | |--- lead_time > 25.50 | | | | | | | | |--- avg_price_per_room <= 71.34 | | | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | | | |--- lead_time <= 68.50 | | | | | | | | | | | |--- weights: [15.66, 78.94] class: 1 | | | | | | | | | | |--- lead_time > 68.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- arrival_month > 3.50 | | | | | | | | | | |--- lead_time <= 102.00 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- lead_time > 102.00 | | | | | | | | | | | |--- weights: [12.67, 3.04] class: 0 | | | | | | | | |--- avg_price_per_room > 71.34 | | | | | | | | | |--- weights: [11.18, 0.00] class: 0 | | | | | |--- avg_price_per_room > 71.92 | | | | | | |--- arrival_year <= 2017.50 | | | | | | | |--- lead_time <= 65.50 | | | | | | | | |--- avg_price_per_room <= 120.45 | | | | | | | | | |--- weights: [79.77, 9.11] class: 0 | | | | | | | | |--- avg_price_per_room > 120.45 | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | |--- weights: [3.73, 12.14] class: 1 | | | | | | | |--- lead_time > 65.50 | | | | | | | | |--- type_of_meal_plan_Meal Plan 2 <= 0.50 | | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | | |--- weights: [16.40, 47.06] class: 1 | | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | |--- type_of_meal_plan_Meal Plan 2 > 0.50 | | | | | | | | | |--- weights: [0.00, 63.76] class: 1 | | | | | | |--- arrival_year > 2017.50 | | | | | | | |--- avg_price_per_room <= 104.31 | | | | | | | | |--- lead_time <= 25.50 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | | |--- weights: [16.40, 0.00] class: 0 | | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | | |--- weights: [38.77, 118.41] class: 1 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- weights: [23.11, 0.00] class: 0 | | | | | | | | |--- lead_time > 25.50 | | | | | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | | |--- weights: [39.51, 185.21] class: 1 | | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | | | | | |--- weights: [73.81, 411.41] class: 1 | | | | | | | |--- avg_price_per_room > 104.31 | | | | | | | | |--- arrival_month <= 10.50 | | | | | | | | | |--- room_type_reserved_Room_Type 5 <= 0.50 | | | | | | | | | | |--- avg_price_per_room <= 195.30 | | | | | | | | | | | |--- truncated branch of depth 9 | | | | | | | | | | |--- avg_price_per_room > 195.30 | | | | | | | | | | | |--- weights: [0.75, 138.15] class: 1 | | | | | | | | | |--- room_type_reserved_Room_Type 5 > 0.50 | | | | | | | | | | |--- arrival_date <= 22.50 | | | | | | | | | | | |--- weights: [11.18, 6.07] class: 0 | | | | | | | | | | |--- arrival_date > 22.50 | | | | | | | | | | | |--- weights: [0.75, 9.11] class: 1 | | | | | | | | |--- arrival_month > 10.50 | | | | | | | | | |--- avg_price_per_room <= 168.06 | | | | | | | | | | |--- lead_time <= 22.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 22.00 | | | | | | | | | | | |--- weights: [17.15, 83.50] class: 1 | | | | | | | | | |--- avg_price_per_room > 168.06 | | | | | | | | | | |--- weights: [12.67, 6.07] class: 0 | | | | |--- required_car_parking_space > 0.50 | | | | | |--- weights: [48.46, 1.52] class: 0 | |--- no_of_special_requests > 0.50 | | |--- no_of_special_requests <= 1.50 | | | |--- market_segment_type_Online <= 0.50 | | | | |--- lead_time <= 102.50 | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | |--- weights: [697.09, 9.11] class: 0 | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | |--- lead_time <= 63.00 | | | | | | | |--- weights: [15.66, 1.52] class: 0 | | | | | | |--- lead_time > 63.00 | | | | | | | |--- weights: [0.00, 7.59] class: 1 | | | | |--- lead_time > 102.50 | | | | | |--- no_of_week_nights <= 2.50 | | | | | | |--- lead_time <= 105.00 | | | | | | | |--- weights: [0.75, 6.07] class: 1 | | | | | | |--- lead_time > 105.00 | | | | | | | |--- weights: [31.31, 13.66] class: 0 | | | | | |--- no_of_week_nights > 2.50 | | | | | | |--- weights: [44.73, 3.04] class: 0 | | | |--- market_segment_type_Online > 0.50 | | | | |--- lead_time <= 8.50 | | | | | |--- lead_time <= 4.50 | | | | | | |--- no_of_week_nights <= 10.00 | | | | | | | |--- weights: [498.03, 40.99] class: 0 | | | | | | |--- no_of_week_nights > 10.00 | | | | | | | |--- weights: [0.00, 3.04] class: 1 | | | | | |--- lead_time > 4.50 | | | | | | |--- arrival_date <= 13.50 | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | |--- weights: [58.90, 36.43] class: 0 | | | | | | | |--- arrival_month > 9.50 | | | | | | | | |--- weights: [33.55, 1.52] class: 0 | | | | | | |--- arrival_date > 13.50 | | | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | | | |--- weights: [123.76, 9.11] class: 0 | | | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | | | |--- avg_price_per_room <= 126.33 | | | | | | | | | |--- weights: [32.80, 3.04] class: 0 | | | | | | | | |--- avg_price_per_room > 126.33 | | | | | | | | | |--- weights: [9.69, 13.66] class: 1 | | | | |--- lead_time > 8.50 | | | | | |--- required_car_parking_space <= 0.50 | | | | | | |--- avg_price_per_room <= 118.55 | | | | | | | |--- lead_time <= 61.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | |--- weights: [70.08, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | | | | | |--- truncated branch of depth 11 | | | | | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [126.74, 1.52] class: 0 | | | | | | | |--- lead_time > 61.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | | | |--- weights: [4.47, 57.69] class: 1 | | | | | | | | | |--- arrival_month > 7.50 | | | | | | | | | | |--- lead_time <= 66.50 | | | | | | | | | | | |--- weights: [5.22, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 66.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | |--- avg_price_per_room <= 71.93 | | | | | | | | | | | |--- weights: [54.43, 3.04] class: 0 | | | | | | | | | | |--- avg_price_per_room > 71.93 | | | | | | | | | | | |--- truncated branch of depth 10 | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | |--- avg_price_per_room > 118.55 | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | |--- arrival_date <= 19.50 | | | | | | | | | |--- no_of_week_nights <= 7.50 | | | | | | | | | | |--- avg_price_per_room <= 177.15 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | | |--- avg_price_per_room > 177.15 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- no_of_week_nights > 7.50 | | | | | | | | | | |--- weights: [0.00, 6.07] class: 1 | | | | | | | | |--- arrival_date > 19.50 | | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | | |--- avg_price_per_room <= 121.20 | | | | | | | | | | | |--- weights: [18.64, 6.07] class: 0 | | | | | | | | | | |--- avg_price_per_room > 121.20 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | | |--- lead_time <= 55.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 55.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | |--- arrival_month > 8.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | |--- weights: [11.93, 10.63] class: 0 | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | |--- weights: [37.28, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- avg_price_per_room <= 119.20 | | | | | | | | | | | |--- weights: [9.69, 28.84] class: 1 | | | | | | | | | | |--- avg_price_per_room > 119.20 | | | | | | | | | | | |--- truncated branch of depth 12 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- lead_time <= 100.00 | | | | | | | | | | | |--- weights: [49.95, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 100.00 | | | | | | | | | | | |--- weights: [0.75, 18.22] class: 1 | | | | | |--- required_car_parking_space > 0.50 | | | | | | |--- weights: [134.20, 1.52] class: 0 | | |--- no_of_special_requests > 1.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_week_nights <= 3.50 | | | | | |--- weights: [1585.04, 0.00] class: 0 | | | | |--- no_of_week_nights > 3.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- no_of_week_nights <= 9.50 | | | | | | | |--- lead_time <= 6.50 | | | | | | | | |--- weights: [32.06, 0.00] class: 0 | | | | | | | |--- lead_time > 6.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- arrival_date <= 5.50 | | | | | | | | | | |--- weights: [23.11, 1.52] class: 0 | | | | | | | | | |--- arrival_date > 5.50 | | | | | | | | | | |--- avg_price_per_room <= 93.09 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- avg_price_per_room > 93.09 | | | | | | | | | | | |--- weights: [77.54, 27.33] class: 0 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [19.38, 0.00] class: 0 | | | | | | |--- no_of_week_nights > 9.50 | | | | | | | |--- weights: [0.00, 3.04] class: 1 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [52.19, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- no_of_special_requests <= 2.50 | | | | | |--- arrival_month <= 8.50 | | | | | | |--- avg_price_per_room <= 202.95 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | | |--- weights: [1.49, 9.11] class: 1 | | | | | | | | |--- arrival_month > 7.50 | | | | | | | | | |--- weights: [8.20, 3.04] class: 0 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- lead_time <= 150.50 | | | | | | | | | |--- weights: [175.20, 28.84] class: 0 | | | | | | | | |--- lead_time > 150.50 | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | |--- avg_price_per_room > 202.95 | | | | | | | |--- weights: [0.00, 10.63] class: 1 | | | | | |--- arrival_month > 8.50 | | | | | | |--- avg_price_per_room <= 153.15 | | | | | | | |--- room_type_reserved_Room_Type 2 <= 0.50 | | | | | | | | |--- avg_price_per_room <= 71.12 | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 71.12 | | | | | | | | | |--- avg_price_per_room <= 90.42 | | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | | |--- weights: [12.67, 7.59] class: 0 | | | | | | | | | |--- avg_price_per_room > 90.42 | | | | | | | | | | |--- weights: [64.12, 60.72] class: 0 | | | | | | | |--- room_type_reserved_Room_Type 2 > 0.50 | | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | |--- avg_price_per_room > 153.15 | | | | | | | |--- weights: [12.67, 3.04] class: 0 | | | | |--- no_of_special_requests > 2.50 | | | | | |--- weights: [67.10, 0.00] class: 0 |--- lead_time > 151.50 | |--- avg_price_per_room <= 100.04 | | |--- no_of_special_requests <= 0.50 | | | |--- no_of_adults <= 1.50 | | | | |--- market_segment_type_Online <= 0.50 | | | | | |--- lead_time <= 163.50 | | | | | | |--- arrival_month <= 5.00 | | | | | | | |--- weights: [2.98, 0.00] class: 0 | | | | | | |--- arrival_month > 5.00 | | | | | | | |--- weights: [0.75, 24.29] class: 1 | | | | | |--- lead_time > 163.50 | | | | | | |--- lead_time <= 341.00 | | | | | | | |--- lead_time <= 173.00 | | | | | | | | |--- arrival_date <= 3.50 | | | | | | | | | |--- weights: [46.97, 9.11] class: 0 | | | | | | | | |--- arrival_date > 3.50 | | | | | | | | | |--- no_of_weekend_nights <= 1.00 | | | | | | | | | | |--- weights: [0.00, 13.66] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 1.00 | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | |--- lead_time > 173.00 | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | |--- arrival_date <= 7.50 | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | | |--- arrival_date > 7.50 | | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | |--- weights: [188.62, 7.59] class: 0 | | | | | | |--- lead_time > 341.00 | | | | | | | |--- weights: [13.42, 27.33] class: 1 | | | | |--- market_segment_type_Online > 0.50 | | | | | |--- avg_price_per_room <= 2.50 | | | | | | |--- lead_time <= 285.50 | | | | | | | |--- weights: [8.20, 0.00] class: 0 | | | | | | |--- lead_time > 285.50 | | | | | | | |--- weights: [0.75, 3.04] class: 1 | | | | | |--- avg_price_per_room > 2.50 | | | | | | |--- weights: [0.75, 97.16] class: 1 | | | |--- no_of_adults > 1.50 | | | | |--- avg_price_per_room <= 82.47 | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | |--- weights: [2.98, 282.37] class: 1 | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | |--- arrival_month <= 11.50 | | | | | | | |--- lead_time <= 244.00 | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | | |--- lead_time <= 166.50 | | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 166.50 | | | | | | | | | | | |--- weights: [2.24, 57.69] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | | |--- weights: [17.89, 0.00] class: 0 | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | | |--- weights: [11.18, 3.04] class: 0 | | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | | |--- weights: [0.00, 12.14] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | |--- weights: [75.30, 12.14] class: 0 | | | | | | | |--- lead_time > 244.00 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- weights: [25.35, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- avg_price_per_room <= 80.38 | | | | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | | | | |--- weights: [11.18, 264.15] class: 1 | | | | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- avg_price_per_room > 80.38 | | | | | | | | | | |--- weights: [7.46, 0.00] class: 0 | | | | | | |--- arrival_month > 11.50 | | | | | | | |--- weights: [46.22, 0.00] class: 0 | | | | |--- avg_price_per_room > 82.47 | | | | | |--- no_of_adults <= 2.50 | | | | | | |--- lead_time <= 324.50 | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | |--- room_type_reserved_Room_Type 4 <= 0.50 | | | | | | | | | |--- weights: [7.46, 986.78] class: 1 | | | | | | | | |--- room_type_reserved_Room_Type 4 > 0.50 | | | | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | | | | |--- weights: [0.00, 10.63] class: 1 | | | | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | | | | |--- weights: [4.47, 0.00] class: 0 | | | | | | | |--- arrival_month > 11.50 | | | | | | | | |--- market_segment_type_Online <= 0.50 | | | | | | | | | |--- weights: [5.22, 0.00] class: 0 | | | | | | | | |--- market_segment_type_Online > 0.50 | | | | | | | | | |--- weights: [0.00, 19.74] class: 1 | | | | | | |--- lead_time > 324.50 | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | |--- weights: [0.75, 13.66] class: 1 | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | |--- no_of_adults > 2.50 | | | | | | |--- weights: [5.22, 0.00] class: 0 | | |--- no_of_special_requests > 0.50 | | | |--- no_of_weekend_nights <= 0.50 | | | | |--- lead_time <= 180.50 | | | | | |--- lead_time <= 159.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- weights: [1.49, 7.59] class: 1 | | | | | |--- lead_time > 159.50 | | | | | | |--- arrival_date <= 1.50 | | | | | | | |--- weights: [1.49, 3.04] class: 1 | | | | | | |--- arrival_date > 1.50 | | | | | | | |--- weights: [35.79, 1.52] class: 0 | | | | |--- lead_time > 180.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- market_segment_type_Online <= 0.50 | | | | | | | |--- avg_price_per_room <= 96.37 | | | | | | | | |--- weights: [12.67, 3.04] class: 0 | | | | | | | |--- avg_price_per_room > 96.37 | | | | | | | | |--- weights: [0.00, 3.04] class: 1 | | | | | | |--- market_segment_type_Online > 0.50 | | | | | | | |--- weights: [7.46, 206.46] class: 1 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [8.95, 0.00] class: 0 | | | |--- no_of_weekend_nights > 0.50 | | | | |--- market_segment_type_Offline <= 0.50 | | | | | |--- arrival_month <= 11.50 | | | | | | |--- avg_price_per_room <= 76.48 | | | | | | | |--- weights: [46.97, 4.55] class: 0 | | | | | | |--- avg_price_per_room > 76.48 | | | | | | | |--- no_of_week_nights <= 6.50 | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | |--- lead_time <= 233.00 | | | | | | | | | | |--- lead_time <= 152.50 | | | | | | | | | | | |--- weights: [1.49, 4.55] class: 1 | | | | | | | | | | |--- lead_time > 152.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- lead_time > 233.00 | | | | | | | | | | |--- weights: [23.11, 19.74] class: 0 | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | |--- weights: [2.24, 15.18] class: 1 | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | |--- lead_time <= 269.00 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- lead_time > 269.00 | | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | |--- no_of_week_nights > 6.50 | | | | | | | | |--- weights: [4.47, 13.66] class: 1 | | | | | |--- arrival_month > 11.50 | | | | | | |--- arrival_date <= 14.50 | | | | | | | |--- weights: [8.20, 3.04] class: 0 | | | | | | |--- arrival_date > 14.50 | | | | | | | |--- weights: [11.18, 31.88] class: 1 | | | | |--- market_segment_type_Offline > 0.50 | | | | | |--- lead_time <= 348.50 | | | | | | |--- weights: [106.61, 3.04] class: 0 | | | | | |--- lead_time > 348.50 | | | | | | |--- weights: [5.96, 4.55] class: 0 | |--- avg_price_per_room > 100.04 | | |--- arrival_month <= 11.50 | | | |--- no_of_special_requests <= 2.50 | | | | |--- weights: [0.00, 3200.19] class: 1 | | | |--- no_of_special_requests > 2.50 | | | | |--- weights: [23.11, 0.00] class: 0 | | |--- arrival_month > 11.50 | | | |--- no_of_special_requests <= 0.50 | | | | |--- weights: [35.04, 0.00] class: 0 | | | |--- no_of_special_requests > 0.50 | | | | |--- arrival_date <= 24.50 | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | |--- arrival_date > 24.50 | | | | | |--- weights: [3.73, 22.77] class: 1
importances = best_model.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
# Training performance comparison
models_train_comp_df = pd.concat(
[
decision_tree_perf_train.T,
decision_tree_tune_perf_train.T,
decision_tree_post_perf_train.T,
],
axis=1,
)
models_train_comp_df.columns = [
"Decision Tree sklearn",
"Decision Tree (Pre-Pruning)",
"Decision Tree (Post-Pruning)",
]
print("Training performance comparison:")
models_train_comp_df
Training performance comparison:
| Decision Tree sklearn | Decision Tree (Pre-Pruning) | Decision Tree (Post-Pruning) | |
|---|---|---|---|
| Accuracy | 0.99421 | 0.99421 | 0.89993 |
| Recall | 0.98661 | 0.98661 | 0.90291 |
| Precision | 0.99578 | 0.99578 | 0.81369 |
| F1 | 0.99117 | 0.99117 | 0.85598 |
# Testing performance comparison
models_test_comp_df = pd.concat(
[
decision_tree_perf_test.T,
decision_tree_tune_perf_test.T,
decision_tree_post_test.T,
],
axis=1,
)
models_test_comp_df.columns = [
"Decision Tree sklearn",
"Decision Tree (Pre-Pruning)",
"Decision Tree (Post-Pruning)",
]
print("Testing performance comparison:")
models_test_comp_df
Testing performance comparison:
| Decision Tree sklearn | Decision Tree (Pre-Pruning) | Decision Tree (Post-Pruning) | |
|---|---|---|---|
| Accuracy | 0.99421 | 0.87118 | 0.86943 |
| Recall | 0.98661 | 0.81175 | 0.85662 |
| Precision | 0.99578 | 0.79461 | 0.76710 |
| F1 | 0.99117 | 0.80309 | 0.80939 |
The model without pruning, the original model seem to have better recall and F1 score. So it will be better that we chose that model to present it to the client.
My recommendations: